A Review on Dimension-Reduction Based Tests For Regressions

Xu Guo and Lixing Zhu ()
Additional contact information
Xu Guo: Beijing Normal University, School of Statistics
Lixing Zhu: Beijing Normal University, School of Statistics

Chapter Chapter 7 in From Statistics to Mathematical Finance, 2017, pp 105-125 from Springer

Abstract: Abstract Curse of dimensionality is a big obstacle for constructing efficient goodness-of-fit tests for regression models with large or moderate number of covariates. To alleviate this difficulty, numerous efforts have been devoted in the last two decades. This review intends to collect and comment on the developments in this aspect. To make the paper self-contained, basic ideas on goodness-of-fit testing for regression models are also briefly reviewed, and the main classes of methods and their advantages and disadvantages are presented. Further, the difficulty caused by the dimensionality (number of covariates) is then discussed. The relevant dimension reduction methodologies are presented. Further, as a dedication to Stute’s 70th birthday, we also include a section to summarize his great contributions other than the results in dimension reduction-based tests.

Keywords: Curse of dimensionality; Dimension reduction; Goodness-of-fit; Model checking; Parametric regression models; Projection pursuit (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-50986-0_7

Ordering information: This item can be ordered from
http://www.springer.com/9783319509860

DOI: 10.1007/978-3-319-50986-0_7

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().